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Post-Stratification
Stratification is a well-known sampling tool built on the premise that like units in a population should be treated similarly. It is a statistical fact that grouping similar units, when sampling, can generally reduce the variance of the survey estimates obtained. Stratification can be done when selecting units for study, or it can be carried out afterward. The latter application is usually termed post-stratification.
Illustration
To illustrate the differences between stratification and post-stratification, assume a researcher is interested in the total poundage of a population that consisted of 10,000 baby mice and one adult elephant. Suppose, further, that the average baby mouse weighted 0.2 pounds but the elephant weighted three and one-half tons or 7,000 pounds. This would mean, if the whole population were to be ...
- Ethical Issues In Survey Research
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- Telephone Consumer Protection Act 1991
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- n
- N
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- Journal of Official Statistics (JOS)
- Kish, Leslie
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- Public Opinion Quarterly (POQ)
- Roper Center for Public Opinion Research
- Roper, Elmo
- Sheatsley, Paul
- Statistics Canada
- Survey Methodology
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- U.S. Bureau of the Census
- World Association for Public Opinion Research (WAPOR)
- Survey Statistics
- Algorithm
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- Mean Square Error
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- Metadata
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- Model-Based Estimation
- Multiple Imputation
- Noncausal Covariation
- Null Hypothesis
- Outliers
- Panel Data Analysis
- Parameter
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- Percentile
- Point Estimate
- Population Parameter
- Post-Survey Adjustments
- Precision
- Probability
- p-Value
- Raking
- Random Assignment
- Random Error
- Raw Data
- Recoded Variable
- Regression Analysis
- Relative Frequency
- Replicate Methods for Variance Estimation
- Research Hypothesis
- Research Question
- Rho
- Sampling Bias
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- Sampling Variance
- SAS
- Seam Effect
- Significance Level
- Solomon Four-Group Design
- Standard Error
- Standard Error of the Mean
- STATA
- Statistic
- Statistical Package for the Social Sciences (SPSS)
- Statistical Power
- SUDAAN
- Systematic Error
- Taylor Series Linearization
- Test-Retest Reliability
- Total Survey Error (TSE)
- t-Test
- Type I Error
- Type II Error
- Unbiased Statistic
- Validity
- Variable
- Variance
- Variance Estimation
- WesVar
- z-Score
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